code: def __init__(self, n_estimators, max_features, random_state=None):
      self.n_estimators = n_estimators
      self.max_features = max_features
      self.random_state = random_state
  
file_path: auto-sklearn/examples/80_extending/example_restrict_number_of_hyperparameters.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.classification
  import autosklearn.pipeline.components.classification
  from autosklearn.pipeline.components.classification import AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE
label_desc: Use image node_id 1 to create a new CustomRandomForest object from inherited base classes: AutoSklearnClassificationAlgorithm with example: obj = CustomRandomForest(n_estimators, max_features, random_state)